Support Vector Machine Detection of Data Framing Attack in Smart Grid

作者：Jiao Wang, Victor O. K. Li · 年份：2018 · DOI：10.1109/cns.2018.8433210 · 被引用次数：5 · 研究领域：Smart Grid Security and Resilience、Network Security and Intrusion Detection、Internet Traffic Analysis and Secure E-voting

Data Framing Attacks (DFA) have been proposed in recent years, and have attracted much interest in the research community on smart grid security. DFA compromises Bad Data Identification and Removal (BDIR, leading BDIR to remove secure data, which will result in incorrect state estimation. Therefore, successful detection of DFA is significantly important for the control and operation of the power grid. This paper presents a study on the utilization of machine learning to detect DFA. Since the detection problem can be formulated as a classification problem between secure measurements and attacked measurements, a mature machine learning technology, Support Vector Machine (SVM) is chosen in this work. The proposed method is examined on the 118-bus IEEE test system. The experimental analyses indicate that SVM can detect DFA with good performance.